Personalized and Safe Route Planning for Asthma Patients Using Real-Time Environmental Data

TL;DR

This paper introduces a health-aware route planning framework for asthmatic patients that uses real-time environmental data to provide safer, personalized outdoor navigation options, outperforming traditional algorithms in accuracy and efficiency.

Contribution

The paper presents a novel heuristic framework utilizing real-time environmental data with an advanced A* algorithm for personalized, safe route planning for asthma patients.

Findings

Outperforms state-of-the-art methods in recommendation accuracy.

Operates with low computational overhead.

Provides dynamic, health-sensitive routing based on real-time data.

Abstract

Asthmatic patients are very frequently affected by the quality of air, climatic conditions, and traffic density during outdoor activities. Most of the conventional routing algorithms, such as Dijkstra's algorithm, usually fail to consider these health dimensions, hence resulting in suboptimal or risky recommendations. Here, the health-aware heuristic framework is presented that shall utilize real-time data provided by the Microsoft Weather API. The advanced A* algorithm provides dynamic changes in routes depending on air quality indices, temperature, traffic density, and other patient-related health data. The power of the model is realized by running simulations in city environments and outperforming the state-of-the-art methodology in terms of recommendation accuracy at low computational overhead. It provides health-sensitive route recommendations, keeping in mind the avoidance of high-risk areas and ensuring safer and more suitable travel options for asthmatic patients.